Fix HF hero CSS and expose Modal endpoint

app.py

@@ -967,9 +967,9 @@ footer {
   overflow: hidden !important;
 }
 
-/* General text */
-.gradio-container .markdown,
-.gradio-container p,
+/* General text - exclude hero */
+.gradio-container .markdown:not(.hero):not(.hero *),
+.gradio-container p:not(.hero p),
 .gradio-container li,
 .gradio-container td,
 .gradio-container th,
@@ -1219,12 +1219,12 @@ footer {
 
 /* EMERGENCY READABILITY FIX */
 
-.gradio-container h1,
-.gradio-container h2,
-.gradio-container h3,
-.gradio-container h4,
-.gradio-container h5,
-.gradio-container h6 {
+.gradio-container h1:not(.hero h1),
+.gradio-container h2:not(.hero h2),
+.gradio-container h3:not(.hero h3),
+.gradio-container h4:not(.hero h4),
+.gradio-container h5:not(.hero h5),
+.gradio-container h6:not(.hero h6) {
     color: #111827 !important;
     opacity: 1 !important;
     visibility: visible !important;
@@ -1536,6 +1536,65 @@ footer {
   color: #111827 !important;
   -webkit-text-fill-color: #111827 !important;
 }
+
+/* HF SPACE HERO FINAL OVERRIDE */
+.gradio-container .hero,
+.gradio-container .hero * {
+  opacity: 1 !important;
+  filter: none !important;
+  mix-blend-mode: normal !important;
+}
+
+.gradio-container .hero h1,
+.gradio-container .hero h1 *,
+.gradio-container .hero p,
+.gradio-container .hero .logo-text {
+  color: #ffffff !important;
+  -webkit-text-fill-color: #ffffff !important;
+}
+
+.gradio-container .hero .kicker {
+  color: #67e8f9 !important;
+  -webkit-text-fill-color: #67e8f9 !important;
+}
+
+.gradio-container .hero h1 span {
+  color: #c4b5fd !important;
+  -webkit-text-fill-color: #c4b5fd !important;
+}
+
+.gradio-container .hero p {
+  color: rgba(255, 255, 255, 0.92) !important;
+  -webkit-text-fill-color: rgba(255, 255, 255, 0.92) !important;
+}
+
+/* ABSOLUTE FINAL HERO FIX FOR HF */
+.gradio-container .hero {
+  color: #ffffff !important;
+}
+
+.gradio-container .hero h1,
+.gradio-container .hero h1 *,
+.gradio-container .hero p,
+.gradio-container .hero p *,
+.gradio-container .hero .logo-text,
+.gradio-container .hero .hero-badge,
+.gradio-container .hero .hero-badge * {
+  color: #ffffff !important;
+  -webkit-text-fill-color: #ffffff !important;
+  opacity: 1 !important;
+  filter: none !important;
+}
+
+.gradio-container .hero .kicker {
+  color: #67e8f9 !important;
+  -webkit-text-fill-color: #67e8f9 !important;
+}
+
+.gradio-container .hero h1 span {
+  color: #c4b5fd !important;
+  -webkit-text-fill-color: #c4b5fd !important;
+}
 """
 
 theme = gr.themes.Soft(

modal_refine.py

@@ -1,12 +1,13 @@
 from __future__ import annotations
 
 import modal
+from fastapi import Request
 
 app = modal.App("paper2lab-nemotron")
 
 image = (
     modal.Image.debian_slim(python_version="3.11")
-    .pip_install("requests")
+    .pip_install("requests", "fastapi")
     .add_local_dir("src/paper2lab", remote_path="/root/paper2lab")
 )
 
@@ -16,70 +17,41 @@ secret = modal.Secret.from_name("nvidia-api-key")
 @app.function(
     image=image,
     secrets=[secret],
-    timeout=180,
+    timeout=300,
 )
-def refine_remote(
-    llm_evidence_pack: dict,
-    model: str = "nvidia/nemotron-3-nano-30b-a3b",
-    return_comparison: bool = True,
-) -> dict:
+@modal.fastapi_endpoint(method="POST")
+async def refine_remote(request: Request):
     from paper2lab.inference.nemotron_refiner import refine_with_nemotron
 
-    return refine_with_nemotron(
-        llm_evidence_pack=llm_evidence_pack,
-        model=model,
-        return_comparison=return_comparison,
-    )
+    body = await request.json()
+
+    llm_evidence_pack = body.get("llm_evidence_pack")
+    model = body.get("model", "nvidia/nemotron-3-nano-30b-a3b")
+    return_comparison = body.get("return_comparison", True)
+
+    if not llm_evidence_pack:
+        return {
+            "status": "error",
+            "error": "Missing llm_evidence_pack",
+        }
+
+    try:
+        result = refine_with_nemotron(
+            llm_evidence_pack=llm_evidence_pack,
+            model=model,
+            return_comparison=return_comparison,
+        )
+        return {
+            "status": "ok",
+            "result": result,
+        }
+    except Exception as exc:
+        return {
+            "status": "error",
+            "error": str(exc),
+        }
 
 
 @app.local_entrypoint()
 def main():
-    sample_pack = {
-        "candidate_paper_card": {
-            "title": "Attention Is All You Need",
-            "field": "Natural Language Processing",
-            "paper_type": "machine_learning",
-            "research_question": "The paper proposes the Transformer architecture.",
-            "contributions": [
-                "The paper proposes a Transformer architecture based on attention."
-            ],
-            "methodology": [
-                "The model uses multi-head self-attention and feed-forward layers."
-            ],
-            "datasets_or_data_sources": [
-                "WMT 2014 English-German dataset",
-                "RNN",
-                "BerkleyParser"
-            ],
-            "models_or_methods": [
-                "Transformer",
-                "multi-head attention"
-            ],
-            "metrics_or_measurements": [
-                "BLEU"
-            ],
-            "key_findings": [],
-            "limitations": [],
-            "missing_reproducibility_info": [
-                "random seed is not specified"
-            ],
-            "metadata": {},
-            "source_pdf": "attention is all you need.pdf",
-            "annotation_version": "v1.0",
-        },
-        "section_previews": [
-            {
-                "title": "Training Data and Batching",
-                "role_hint": "methodology",
-                "page_start": 7,
-                "page_end": 7,
-                "preview": "We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences."
-            }
-        ],
-        "captions": [],
-        "tables": [],
-        "metadata": {}
-    }
-
-    result = refine_remote.remote(sample_pack)
-    print(result)
+    print("Deploy this app with: modal deploy modal_refine.py")